Who is on the rise in Austria: Wage mobility and mobility risk

In this paper we investigate earnings mobility in Austria from the angle of individual persons. Earnings mobility over time has two aspects: Positional changes and the volatility of earnings over time. Whereas the further is a positive outcome, more volatility as such can be seen as negative. We use Austrian data from tax authorities to find out how population characteristics are related to these two concepts of earnings mobility. Our main results concern initial positions: The higher up you are at the beginning, the more difficult is further positional advancement, moreover, in terms of volatility we see that those in the lowest quantiles of the earnings distribution face the highest volatility of earnings positions over time. *Rudolf Winter-Ebmer is also associated with CEPR, London and IZA, Bonn.     

Regression-based,regression-free and model-free approaches for robust online scale estimation

This paper compares the methods for variability extraction from a univariate time series in real time. The online scale estimation is achieved by applying a robust scale functional to a moving time window. Scale estimators based on the residuals of a preceding regression step are compared with regression-free and model-free techniques in a simulation study and in an application to a real time series. In the presence of level shifts or strong non-linear trends in the signal level, the model-free scale estimators perform especially well. However, the investigated regression-free and regression-based methods have higher breakdown points, they are applicable to data containing temporal correlations, and they are much more efficient.     

石油价格冲击与经济波动风险最小化的货币供应机制分析

本文通过构建一个包含石油价格冲击的DSGE模型,基于经济波动风险的最小化,研究了石油价格冲击对中国货币供应机制的影响。在模型结构参数贝叶斯估计的基础上,通过货币政策前沿的比较分析,回答了中国货币供应机制是否应该对石油价格冲击做出反应以及应该如何反应的政策问题。研究结果表明,中国当前的货币供应机制并没有对石油价格冲击做出显著的反应,但为了减小经济波动的风险,中国的货币供应机制在对产出增长和通货膨胀反应的同时,对石油价格冲击做出反应具有必要性。     

股市波动的杠杆效应检验:一种新的方法

股市波动的非对称性特征一直是金融研究中关注的焦点问题。本文首次构建了一个非平衡似无关波动模型,从个股角度对波动的非对称性进行检验。通过与综合指数的对比研究,本文揭示了市场因素对波动非对称性的影响。实证结果表明,我国深证成份指数波动存在杠杆效应,而成份股波动却表现出反向杠杆效应。市场同时存在的共同因素和异质因素,是导致成份股波动和成份指数波动表现不一致的原因。进一步的研究结果表明,在消除共同因素影响后,成份股波动的反向杠杆效应会表现更明显。     

隐Markov链驱动关联性和波动性的传染分析

本文将隐Markov链对波动性和相关性的驱动分析引入DCC多元GARCH,对波动和相关分析建立起了直接的联系,进而考察次贷危机、欧洲债务危机在主要证券市场间的传染性。研究发现,高波动高相关机制为联动性提供了一种直接的表述方式,且这一机制在危机期间处于支配地位;次贷危机、欧洲债务危机具有传染性,传染期以区间的形式出现,且危机初期的市场在各机制间有较为频繁的转换,不可根据危机事件对样本进行武断地分割;同时,危机的传染在所考察的市场之间具有系统性,应对危机需要各国政策间的协调配合;另外,有证据显示美国次贷市场在2006年年中已显现出问题,有关国家贻误了深入分析和应对危机的时机。     

沪深300指数期货的价格发现和波动溢出效应研究

我国沪深300指数期货推出后, 股指期货的价格发现功能以及对现货市场波动性的影响成为学术界的关注焦点。基于Johansen协整分析、向量误差修正模型和带有误差修正的双变量EGARCH模型, 对沪深300指数期货市场和现货市场之间的价格发现功能以及互动关系进行了深入的研究和分析, 结果显示:沪深300指数现货市场居于价格发现的主导地位;沪深300指数期货交易减弱了现货市场的条件波动。可见, 虽然沪深300指数期货的价格发现功能并未得到应有的发挥, 然而熨平股指波动的功能却得到了体现。     

An empirical Bayesian forecast in the threshold stochastic volatility models

In the area of finance, the stochastic volatility (SV) model is a useful tool for modelling stock market returns. However, there is evidence that asymmetric behaviour of stock returns exists. A threshold SV (THSV) model is provided to capture this behaviour. In this study, we introduce a robust model created through empirical Bayesian analysis to deal with the uncertainty between the SV and THSV models. A Markov chain Monte Carlo algorithm is applied to empirically select the hyperparameters of the prior distribution. Furthermore, the value at risk from the resulting predictive distribution is also given. Simulation studies show that the proposed empirical Bayes model not only clarifies the acceptability of prediction but also reduces the risk of model uncertainty.     

Stochastic volatility models for exchange rates and their estimation using quasi-maximum-likelihood methods: an application to the South African Rand

This paper is concerned with the volatility modeling of a set of South African Rand (ZAR) exchange rates. We investigate the quasi-maximum-likelihood (QML) estimator based on the Kalman filter and explore how well a choice of stochastic volatility (SV) models fits the data. We note that a data set from a developing country is used. The main results are: (1) the SV model parameter estimates are in line with those reported from the analysis of high-frequency data for developed countries; (2) the SV models we considered, along with their corresponding QML estimators, fit the data well; (3) using the range return instead of the absolute return as a volatility proxy produces QML estimates that are both less biased and less variable; (4) although the log range of the ZAR exchange rates has a distribution that is quite far from normal, the corresponding QML estimator has a superior performance when compared with the log absolute return.     

Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model

I introduce the notion of continuous invertibility on a compact set for volatility models driven by a stochastic recurrence equation. I prove strong consistency of the quasi‐maximum likelihood estimator (QMLE) when the quasi‐likelihood criterion is maximized on a continuously invertible domain. This approach yields, for the first time, the asymptotic normality of the QMLE for the exponential general autoregressive conditional heteroskedastic (EGARCH(1,1)) model under explicit but non‐verifiable conditions. In practice, I propose to stabilize the QMLE by constraining the optimization procedure to an empirical continuously invertible domain. The new method, called stable QMLE, is asymptotically normal when the observations follow an invertible EGARCH(1,1) model.     

Bayesian Analysis of Stochastic Volatility Models

New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance states is a by-product of our Markov-chain method. In addition, multistep-ahead predictive densities can be constructed that reflect both inherent model variability and parameter uncertainty. We illustrate our method by analyzing both daily and weekly data on stock returns and exchange rates. Sampling experiments are conducted to compare the performance of Bayes estimators to method of moments and quasi-maximum likelihood estimators proposed in the literature. In both parameter estimation and filtering, the Bayes estimators outperform these other approaches.